Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the expected information gain (EIG) on model…
Bayesian OptimizationDensity EstimationExperimental DesignEnabling Uncertainty Estimation in Iterative Neural Networks
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional …
Bayesian OptimizationOut-of-Distribution DetectionUncertainty QuantificationGuided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on av…
Bayesian OptimizationGaussian ProcessesMultiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snaps…
Bayesian OptimizationGraph Bayesian Optimization for Multiplex Influence Maximization
Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on…
Bayesian OptimizationModel Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational…
Bayesian OptimizationOn Safety in Safe Bayesian Optimization
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimization (BO) is used for this. Due to the saf…
Bayesian OptimizationAutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly. However, explorin…
Bayesian OptimizationHigh-Level SynthesisChain-structured neural architecture search for financial time series forecasting
Neural architecture search (NAS) emerged as a way to automatically optimize neural networks for a specific task and dataset. Despite an abundance of research on NAS for images and natural language applications, similar s…
Bayesian OptimizationNeural Architecture Searchreinforcement-learningReinforcement Learning+2Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. Furtherm…
Bayesian OptimizationBayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPR
Optimization of product and system characteristics is required in many fields, including design and control. Bayesian optimization (BO) is often used when there are high observing costs, because BO theoretically guarante…
Bayesian OptimizationGPRregressionMechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
This research delves into optimizing mechanism design, with an emphasis on the energy efficiency and the expansive design possibilities of reciprocating mechanisms. It investigates how to efficiently integrate Computer-A…
Bayesian OptimizationGaussian ProcessesRobustifying and Boosting Training-Free Neural Architecture Search
Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training-free NAS as an emerging paradigm has successfully reduced the s…
AutoMLBayesian OptimizationNeural Architecture SearchPMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates
We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO). The algorithm alternates polynomial approximation with Bayesian optimization steps, using Gaussian processe…
Bayesian OptimizationEvolutionary AlgorithmsGaussian ProcessesCost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality
Tuning searches are pivotal in High-Performance Computing (HPC), addressing complex optimization challenges in computational applications. The complexity arises not only from finely tuning parameters within routines but …
Bayesian OptimizationGPUIn-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization. However, this method is hinder…
Bayesian InferenceBayesian OptimizationIn-Context Learningreinforcement-learning+2FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables
Feature augmentation from one-to-many relationship tables is a critical but challenging problem in ML model development. To augment good features, data scientists need to come up with SQL queries manually, which is time-…
AttributeBayesian OptimizationHyperparameter OptimizationBEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems
The rigorous safety verification of control systems in critical applications is essential, given their increasing complexity and integration into everyday life. Simulation-based falsification approaches play a pivotal ro…
Bayesian OptimizationAn Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization
Bayesian optimization (BO) has shown impressive results in a variety of applications within low-to-moderate dimensional Euclidean spaces. However, extending BO to high-dimensional settings remains a significant challenge…
Bayesian OptimizationDimensionality ReductionExplaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to p…
Bayesian OptimizationGaussian Processes